Towards Enhancement of the Economy of a Thermal Power Generating System through Prediction of Plant Efficiency

The plant ‘Heat Rate’ (HR) is a measure of overall efficiency of a thermal power generating system. It depends on a large number of factors, some of which are non-measurable, while data relating to others are seldom available and recorded. However, coal quality (expressed in terms of ‘effective heat value’ (EHV) as kcal/kg) transpires to be one of the important factors that influences HR values and data on EHV are available in any thermal power generating system. In the present work, we propose a prediction interval of the HR values on the basis of only EHV, keeping in mind that coal quality is one of the important (but not the only) factors that have a pronounced effect on the combustion process and hence on HR. The underlying theory borrows the idea of providing simultaneous confidence interval (SCI) to the coefficients of a p-th p(≥1) order autoregressive model (AR(p)). The theory has been substantiated with the help of real life data from a power utility (after suitable base and scale transformation of the data to maintain the confidentiality of the classified document). Scope for formulating strategies to enhance the economy of a thermal power generating system has also been explored.     

Correlated random effects regression analysis for a log-normally distributed variable

In regression analysis, it is assumed that the response (or dependent variable) distribution is Normal, and errors are homoscedastic and uncorrelated. However, in practice, these assumptions are rarely satisfied by a real data set. To stabilize the heteroscedastic response variance, generally, log-transformation is suggested. Consequently, the response variable distribution approaches nearer to the Normal distribution. As a result, the model fit of the data is improved. Practically, a proper (seems to be suitable) transformation may not always stabilize the variance, and the response distribution may not reduce to Normal distribution. The present article assumes that the response distribution is log-normal with compound autocorrelated errors. Under these situations, estimation and testing of hypotheses regarding regression parameters have been derived. From a set of reduced data, we have derived the best linear unbiased estimators of all the regression coefficients, except the intercept which is often unimportant in practice. Unknown correlation parameters have been estimated. In this connection, we have derived a test rule for testing any set of linear hypotheses of the unknown regression coefficients. In addition, we have developed the confidence ellipsoids of a set of estimable functions of regression coefficients. For the fitted regression equation, an index of fit has been proposed. A simulated study illustrates the results derived in this report.     

Sequential estimation problems for negative exponential populations

The literature on sequential estimation problems for negative exponential populations has been reviewed here, We attempt to bring in all the published and unpublished materials known to us in a fairly coherent fashion. Both the concepts and theoretical findings are discussed.     

Quantile dispersion graphs to compare the efficiencies of cluster randomized designs

The purpose of this article is to compare efficiencies of several cluster randomized designs using the method of quantile dispersion graphs (QDGs). A cluster randomized design is considered whenever subjects are randomized at a group level but analyzed at the individual level. A prior knowledge of the correlation existing between subjects within the same cluster is necessary to design these cluster randomized trials. Using the QDG approach, we are able to compare several cluster randomized designs without requiring any information on the intracluster correlation. For a given design, several quantiles of the power function, which are directly related to the effect size, are obtained for several effect sizes. The quantiles depend on the intracluster correlation present in the model. The dispersion of these quantiles over the space of the unknown intracluster correlation is determined, and then depicted by the QDGs. Two applications of the proposed methodology are presented.     

Design and implementation of an integrated production planning system for a pharmaceutical manufacturing concern in India

SmithKline Beecham (SBPI) is a major player in the Indian pharmaceutical industry. Though relatively small in terms of turnover, it has performed Pharmaceuticals (India) exceptionally well in terms ofprofitability and growth rate. The firm has substantial cash reserves. However, the firm operates in 'mass production' mode at present, with the established finished goods inventory norms being in the range of 1 to 1.5 months. Competitive pressure on the firm is now forcing it to move towards world class manufacturing and the application of concepts from MRP II, JIT and OPT is under consideration. This paper describes the considerations that went into the justification, design and implementation of an integrated production planning and control system in this situaion.     

Optimality in the presence of two factor interactions among the nuisance factors

Here we consider an m way heterogeneity settingin the presence of two factor interactions among the heterogeneity directions . The set of experimental units considered do not exhaust all possible level combinations of the heterogeneity directions ; but the set is such that all heterogeneity effects assumed i n the model are orthogonally estmable.In such a setting , calleda doubly balanced m-way setting , the C-matrix of the reduced normal equations for the treatment effects is derived . Universally optimal designs are obtained in the cases where the settingis (i) Completely regular or (ii) partly regular of a special type . An interesting observation is that there are situations where the universally optimal designsin the present setting are totally different from the designs known t o be universally optimal when there is no interaction effect among the heterogeneity directions. This indicates that the usual optimality criteria are sensitive to validity or otherwise of the usual assumptions of lack of interactions among heterogeneity directions.     

Maximizing Business Returns to Corporate Social Responsibility (CSR): The Role of CSR Communication

By engaging in corporate social responsibility (CSR) activities, companies can not only generate favorable stakeholder attitudes and better support behaviors (e.g. purchase, seeking employment, investing in the company), but also, over the long run, build corporate image, strengthen stakeholder–company relationships, and enhance stakeholders' advocacy behaviors. However, stakeholders' low awareness of and unfavorable attributions towards companies' CSR activities remain critical impediments in companies' attempts to maximize business benefits from their CSR activities, highlighting a need for companies to communicate CSR more effectively to stakeholders. In light of these challenges, a conceptual framework of CSR communication is presented and its different aspects are analyzed, from message content and communication channels to company- and stakeholder-specific factors that influence the effectiveness of CSR communication.     

Multistage methodologies for comparing several treatments with a control

We consider the problem of constructing a set of fixed-width simultaneous confidence intervals for the treatment-control differences of means for several independent normal populations with a common unknown variance. Taking c observations from the control population instead of the usual vector-at-a-time approach, purely sequential estimation methodology is developed and asymptotic second-order characteristics are provided. Brief remarks on the accelerated sequential and three-stage methodologies have been added. Next, with the help of simulations, performances of the purely sequential, accelerated sequential and three-stage estimation techniques are compared. Overall, the second-order asymptotics are found to provide useful approximations even for moderate sample sizes.     

On Sharp Jensen's Inequality and Some Unusual Applications

The purpose of this article is two-fold. First, we find it very interesting to explore a kind of notion of optimality of the customary Jensen-bound among all Jensen-type bounds. Without this result, the customary Jensen-bound stood alone simply as just another bound. The proposed notion and the associated optimality are important given that in some situations the Jensen's inequality does leave us empty handed.

When it comes to highlighting Jensen's inequality, unfortunately only a handful of nearly routine applications continues to recycle time after time. Such encounters rarely produce any excitement. This article may change that outlook given its second underlying purpose, which is to introduce a variety of unusual applications of Jensen's inequality. The collection of our important and useful applications and their derivations are new.